Overview

This is a trading strategy based on analyzing the historical highs and lows of the Delta SMA (Simple Moving Average) of buy/sell volumes over a one-year period. The strategy identifies potential trading signals by comparing the Delta SMA with historical threshold values. It employs a long-term lookback period, making it suitable for medium to long-term trend trading.

Strategy Principles

The core logic of the strategy is based on the following key steps:

1. Delta Calculation: Calculate the difference between buy and sell volumes based on price movement. Volume is recorded as buy volume when closing price is above opening price, and vice versa.
2. SMA Smoothing: Apply a 14-period moving average to the Delta value to reduce noise.
3. One-Year High/Low Determination: Calculate the highest and lowest values of Delta SMA over the past year.
4. Signal Trigger Conditions:
   • Buy Signal: Triggered when Delta SMA crosses above 0 after falling below 70% of the yearly low
   • Sell Signal: Triggered when Delta SMA falls below 60% after crossing above 90% of the yearly high

Strategy Advantages

1. Strong Long-term Trend Capture: Effectively captures major trends through one-year historical data analysis.
2. Excellent Noise Filtering: Uses SMA smoothing and multiple threshold conditions to effectively reduce false signals.
3. Reasonable Risk Control: Sets clear entry and exit conditions to avoid overtrading.
4. High Adaptability: Strategy parameters can be adjusted for different market conditions.

Strategy Risks

1. Lag Risk: Use of SMA and long lookback period may lead to delayed signals.
2. False Breakout Risk: May generate false signals in ranging markets.
3. Market Environment Dependency: May underperform in markets without clear trends.
4. Parameter Sensitivity: Threshold settings significantly impact strategy performance.

Strategy Optimization Directions

1. Dynamic Threshold Adjustment: Dynamically adjust high/low thresholds based on market volatility.
2. Additional Indicators: Incorporate other technical indicators to improve signal reliability.
3. Stop-Loss Implementation: Implement dynamic stop-loss mechanisms for risk control.
4. Market Environment Filtering: Add market environment assessment logic to run the strategy in suitable conditions.

Summary

This is a medium to long-term trend following strategy based on volume analysis, capturing market trends by analyzing historical highs and lows of buy/sell volume differences. The strategy is well-designed with proper risk control, but attention needs to be paid to market environment adaptability and parameter optimization. Through the proposed optimization directions, there is room for further strategy improvement.